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KQL Series – quick intro to Azure Data Explorer


Azure Data Explorer is a PaaS offering from Azure providing an end-to-end solution for data exploration.

Here is a quick introduction of the features:

This service from Azure was developed to provide end-to-end data exploration services to help the businesses get quick insights, and make critical business decisions. It can be used for streaming data as well to identify patterns, statistics, anomalies, outliers, and even to diagnose issues.

ADX is a fully managed data analytics service for near real-time analysis on large volumes of data streaming (i.e. log and telemetry data) from such sources as applications, websites, or IoT devices.  ADX makes it simple to ingest this data and enables you to perform complex ad-hoc queries on the data in seconds – ADX has speeds of up to 200MB/sec per node (up to 1,000 nodes) and queries across a billion records take less than a second (!!).  A typical use case is when you are generating terabytes of data from which you need to understand quickly what that data is telling you, as opposed to a traditional database that takes longer to get value out of the data because of the effort to collect the data and place it in the database before you can start to explore it. This allows us to iterate our queries really, really quickly.

Azure Data Explorer has a very fast indexing and a very powerful query language to work with the data. You guessed it – that language is Kusto Query Language!!.

Azure Data Explorer aka ADX, is a fast, highly scalable and fully managed data analytics service for log, telemetry and streaming data. This data exploration service enables you to pull together, store and analyze diverse data. You can query terabytes of data in a few seconds and it allows fast ad-hoc queries over the varied data.

ADX is a PaaS offering from Azure, which is capable of performing analysis on large volumes of data from heterogeneous sources, like – Custom Applications, IoT Devices, Diagnostic Logs, and other streaming data sources as well. This data can be structured, unstructured, or free text.

Data within ADX is organized in relational tables within the database, which has a strongly typed schema. It can scale quickly depending upon the ingested data volume and query load.

ADX works on the principle of isolation between Compute and Storage using volatile SSD storage as a cache and persistent storage in Azure Blob Storage. It is a fully managed ‘Platform as a Service (PaaS)’ that lets users focus only on their data and queries.

Azure Data Explorer can be used with other Azure offerings to create end-to-end solutions for various use cases.

Let’s have a look at some of those use cases for ADX.


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